【Hadoop离线基础总结】流量日志分析网站整体架构模块开发

数据仓库设计

维度建模概述

维度建模 (dimensional modeling) 是专门用于分析型数据库、数据仓库、数据集市建模（数据集市可以理解为是一种"小型数据仓库"）的方法。
维度表 (dimension) 就是对数据按类别、区域等各个角度进行分析，比如有一个实例：今天下午白某在星巴克花40元喝了一杯星冰乐。以消费为主题对这段信息可以提取四个维度：时间维度 （今天下午）、地点维度 （星巴克）、价格维度 （40元）、商品维度 （星冰乐）。所以一般情况下维度表信息比较固定，且数据量小。
事实表 (fact table) 包含了与各维度表相关联的外键，并通过JOIN方式与维度表关联。事实表的度量通常是数值类型，且记录数会不断增加，表规模迅速增长。
数据仓库的主导功能是面向分析，以查询为主，不涉及数据更新操作，所以不需要严格遵守规范化设计原则。事实表的设计是以能够正确记录历史信息为准则，维度表的设计是以能够以合适的角度来聚合主题内容为准则。
维度建模的三种模式

1.星型模式

星型模式 (Star Schema) 是以事实表为中心，所有的维度表直接连接在事实表上，像星星一样，是最常用的维度建模方式。
特点：
1.维度表只和事实表关联，维度表之间没有关联
2.每个维度表主键为单列，且该主键放置在事实表中，作为两边连接的外键
3.以事实表为核心，维表围绕核心呈星形分布

2.雪花模式

雪花模式 (Snowflake Schema) 是对星形模式的扩展。雪花模式的维度表可以拥有其他维度表的，虽然这种模型相比星型更规范一些，但是由于这种模型不太容易理解，维护成本比较高，而且性能方面需要关联多层维表，性能也比星型模型要低。所以一般不是很常用。

3.星座模式

星座模式是星型模式延伸而来，星型模式是基于一张事实表的，而星座模式是基于多张事实表的，而且共享维度信息。在业务发展后期，绝大部分维度建模都采用的是星座模式。
本项目中数据仓库的设计

为了方便统计，将time_local拆分为daystr,timestr,month,day,hour五个字段，将http_referer拆分为ref_host,ref_path,ref_query,ref_query_id四个字段

ETL开发

创建ODS层数据表

原始日志数据表

create database weblog;

create table ods_weblog_origin(
valid string,
remote_addr string,
remote_user string,
time_local string,
request string,
status string,
body_bytes_sent string,
http_referer string,
http_user_agent string)
partitioned by (datestr string)
row format delimited
fields terminated by '\001';

点击流模型pageviews表

create table ods_click_pageviews(
session string,
remote_addr string,
remote_user string,
time_local string,
request string,
visit_step string,
page_staylong string,
http_referer string,
http_user_agent string,
body_bytes_sent string,
status string)
partitioned by (datestr string)
row format delimited
fields terminated by '\001';

点击流模型visit表

create table ods_click_stream_visit(
session     string,
remote_addr string,
inTime      string,
outTime     string,
inPage      string,
outPage     string,
referal     string,
pageVisits  int)
partitioned by (datestr string)
row format delimited
fields terminated by '\001';

导入ODS层数据

导入清洗结果数据到贴源数据表ods_weblog_origin

LOAD DATA LOCAL inpath '/export/servers/weblog/weblogout' overwrite INTO TABLE ods_weblog_origin PARTITION ( datestr = '20130918' );

导入点击流模型pageviews数据到ods_click_pageviews表

LOAD DATA LOCAL inpath '/export/servers/weblog/pageView' overwrite INTO TABLE ods_click_pageviews PARTITION ( datestr = '20130918' );

导入点击流模型visit数据到ods_click_stream_visit表
```
LOAD DATA LOCAL inpath '/export/servers/weblog/visit' overwrite INTO TABLE ods_click_stream_visit PARTITION ( datestr = '20130918' );
```
生产环境中应该将数据load命令写在脚本中，然后配置在azkaban定时运行，注意运行的时间点，应该在预处理数据完成之后。

生成ODS层明细宽表

建明细表ods_weblog_detail

CREATE TABLE ods_weblog_detail(
valid           string, --有效标识
remote_addr     string, --来源IP
remote_user     string, --用户标识
time_local      string, --访问完整时间
daystr          string, --访问日期
timestr         string, --访问时间
month           string, --访问月
day             string, --访问日
hour            string, --访问时
request         string, --请求的url
status          string, --响应码
body_bytes_sent string, --传输字节数
http_referer    string, --来源url
ref_host        string, --来源的host
ref_path        string, --来源的路径
ref_query       string, --来源参数query
ref_query_id    string, --来源参数query的值
http_user_agent string --客户终端标识
)
partitioned BY ( datestr string );

通过查询插入数据到明细宽表 ods_weblog_detail中

抽取refer_url到中间表 t_ods_tmp_referurl

CREATE TABLE t_ods_tmp_referurl AS 
SELECT a.*,b.* 
FROM ods_weblog_origin a 
LATERAL VIEW parse_url_tuple (
regexp_replace ( http_referer, "\"", "" ),
'HOST','PATH','QUERY','QUERY:id' ) b 
AS host,path,query,query_id;

lateral view要和split, explode等UDTF一起使用，它能够将一列数据拆成多行数据。

抽取转换time_local字段到中间表明细表 t_ods_tmp_detail

CREATE TABLE t_ods_tmp_detail AS
SELECT b.*,
substring( time_local, 0, 10 ) AS daystr,
substring( time_local, 12 ) AS timestr,
substring( time_local, 6, 2 ) AS month,
substring( time_local, 9, 2 ) AS day,
substring( time_local, 11, 3 ) AS hour 
FROM t_ods_tmp_referurl b;

可以将上面两个合成一个sql语句

INSERT INTO TABLE ods_weblog_detail PARTITION ( datestr = '20130918' )
SELECT c.valid,c.remote_addr,c.remote_user,c.time_local,
substring( c.time_local, 0, 10 ) AS daystr,
substring( c.time_local, 12 ) AS timestr,
substring( c.time_local, 6, 2 ) AS month,
substring( c.time_local, 9, 2 ) AS day,
substring( c.time_local, 11, 3 ) AS hour,
c.request,c.status,c.body_bytes_sent,c.http_referer,c.ref_host,c.ref_path,c.ref_query,c.ref_query_id,c.http_user_agent 
FROM (
SELECT a.valid,a.remote_addr,a.remote_user,a.time_local,a.request,a.status,a.body_bytes_sent,a.http_referer,a.http_user_agent,
b.ref_host,b.ref_path,b.ref_query,b.ref_query_id 
FROM ods_weblog_origin a 
LATERAL VIEW parse_url_tuple ( regexp_replace ( http_referer, "\"", "" ),
'HOST','PATH','QUERY','QUERY:id' ) b 
AS ref_host,ref_path,ref_query,ref_query_id ) c;

不能这么写，会报错

INSERT INTO TABLE ods_weblog_detail PARTITION (datestr='20130918')
SELECT c.*,
substring( c.time_local, 0, 10 ) AS daystr,
substring( c.time_local, 12 ) AS timestr,
substring( c.time_local, 6, 2 ) AS month,
substring( c.time_local, 9, 2 ) AS day,
substring( c.time_local, 11, 3 ) AS hour
FROM (
SELECT a.*,b.* FROM ods_weblog_origin a
LATERAL VIEW parse_url_tuple ( 
regexp_replace ( http_referer, "\"", "" ),'HOST','PATH','QUERY','QUERY:id' ) b
AS ref_host,ref_path,ref_query,ref_query_id ) c;

报错信息：Line 1:18 Cannot insert into target table because column number/types are different ‘‘20130918’’: Table insclause-0 has 18 columns, but query has 19 columns.

统计分析开发

流量分析

统计每小时的PVS（pageView访问量）

创建统计每小时PVS表

CREATE TABLE dw_pvs_everyhour_oneday (
month string,
day string,
hour string,
pvs BIGINT ) 
partitioned BY ( datestr string );

统计每小时的PVS

INSERT INTO TABLE dw_pvs_everyhour_oneday PARTITION (datestr = '20130918')
SELECT a.month AS month, a.day AS day, a.hour AS hour,count(*) AS pvs
FROM ods_weblog_detail a
WHERE a.datestr = '20130918'
GROUP BY a.month,a.day,a.hour;

查询结果

+--------------------------------+------------------------------+-------------------------------+------------------------------+----------------------------------+--+
| dw_pvs_everyhour_oneday.month  | dw_pvs_everyhour_oneday.day  | dw_pvs_everyhour_oneday.hour  | dw_pvs_everyhour_oneday.pvs  | dw_pvs_everyhour_oneday.datestr  |
+--------------------------------+------------------------------+-------------------------------+------------------------------+----------------------------------+--+
| 09                             | 18                           |  06                           | 111                          | 20130918                         |
| 09                             | 18                           |  07                           | 1010                         | 20130918                         |
| 09                             | 18                           |  08                           | 2052                         | 20130918                         |
| 09                             | 18                           |  09                           | 1374                         | 20130918                         |
| 09                             | 18                           |  10                           | 568                          | 20130918                         |
| 09                             | 18                           |  11                           | 571                          | 20130918                         |
| 09                             | 18                           |  12                           | 621                          | 20130918                         |
| 09                             | 18                           |  13                           | 531                          | 20130918                         |
| 09                             | 18                           |  14                           | 514                          | 20130918                         |
| 09                             | 18                           |  15                           | 759                          | 20130918                         |
| 09                             | 18                           |  16                           | 475                          | 20130918                         |
| 09                             | 18                           |  17                           | 382                          | 20130918                         |
| 09                             | 18                           |  18                           | 262                          | 20130918                         |
| 09                             | 18                           |  19                           | 390                          | 20130918                         |
| 09                             | 18                           |  20                           | 211                          | 20130918                         |
| 09                             | 18                           |  21                           | 213                          | 20130918                         |
| 09                             | 18                           |  22                           | 351                          | 20130918                         |
| 09                             | 18                           |  23                           | 382                          | 20130918                         |
| 09                             | 19                           |  00                           | 312                          | 20130918                         |
| 09                             | 19                           |  01                           | 324                          | 20130918                         |
| 09                             | 19                           |  02                           | 546                          | 20130918                         |
| 09                             | 19                           |  03                           | 552                          | 20130918                         |
| 09                             | 19                           |  04                           | 569                          | 20130918                         |
| 09                             | 19                           |  05                           | 540                          | 20130918                         |
| 09                             | 19                           |  06                           | 150                          | 20130918                         |
+--------------------------------+------------------------------+-------------------------------+------------------------------+----------------------------------+--+

统计每天的PVS

创建统计每天PVS的表

CREATE TABLE dw_pvs_everyday (
month string,
day string,
pvs BIGINT ) 
partitioned BY ( datestr string );

统计每天的PVS

INSERT INTO TABLE dw_pvs_everyday PARTITION (datestr = '20130918')
SELECT a.month AS month, a.day AS day,COUNT(*) AS pvs
FROM ods_weblog_detail a
WHERE a.datestr = '20130918'
GROUP BY a.month,a.day;

查询结果

+--------+------+--------+--+
| month  | day  |  pvs   |
+--------+------+--------+--+
| 09     | 18   | 10777  |
| 09     | 19   | 2993   |
+--------+------+--------+--+

统计每小时各来访url产生的pv量

创建表

CREATE TABLE dw_pvs_referer_everyhour (
referer_url string,
referer_host string,
month string,
day string,
hour string,
pv_referer_cnt BIGINT ) 
partitioned BY ( datestr string );

统计每小时各来访url产生的pv量

INSERT INTO TABLE dw_pvs_referer_everyhour
PARTITION (datestr='20130918')
SELECT http_referer,ref_host,month,day,hour,count(1) AS pv_referer_cnt
FROM ods_weblog_detail
GROUP BY http_referer,ref_host,month,day,hour
HAVING ref_host IS NOT NULL
ORDER BY hour asc,day asc,month asc,pv_referer_cnt desc;

查询结果(limit 10)

+--------+------+-------+-------------------+----------------------------------------------------+-----------------+--+
| month  | day  | hour  |   referer_host    |                    referer_url                     | pv_referer_cnt  |
+--------+------+-------+-------------------+----------------------------------------------------+-----------------+--+
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/mongodb-replica-set/"         | 20              |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/vps-ip-dns/"                  | 19              |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/nodejs-grunt-intro/"          | 7               |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/nodejs-socketio-chat/"        | 7               |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/wp-content/themes/silesia/style.css" | 7               |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/nodejs-async/"                | 5               |
| 09     | 18   |  06   | www.angularjs.cn  | "http://www.angularjs.cn/A00n"                     | 2               |
| 09     | 18   |  06   | blog.fens.me      | "http://blog.fens.me/nodejs-express3/"             | 2               |
| 09     | 18   |  06   | www.angularjs.cn  | "http://www.angularjs.cn/"                         | 1               |
| 09     | 18   |  06   | www.google.com    | "http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&cad=rja&ved=0CHIQFjAF&url=http%3A%2F%2Fblog.fens.me%2Fvps-ip-dns%2F&ei=j045UrP5AYX22AXsg4G4DQ&usg=AFQjCNGsJfLMNZnwWXNpTSUl6SOEzfF6tg&sig2=YY1oxEybUL7wx3IrVIMfHA&bvm=bv.52288139,d.b2I" | 1               |
+--------+------+-------+-------------------+----------------------------------------------------+-----------------+--+

统计每小时各来访host的产生的pv数并排序

建表

CREATE TABLE dw_pvs_refererhost_everyhour (
ref_host string,
month string,
day string,
hour string,
ref_host_cnts BIGINT ) 
partitioned BY ( datestr string );

统计每小时各来访host的产生的pv数并排序

INSERT INTO TABLE dw_pvs_refererhost_everyhour
PARTITION ( datestr = '20130918' )
SELECT ref_host,month,day,hour,count(1) AS ref_host_cnts
FROM ods_weblog_detail
GROUP BY ref_host,month,day,hour
HAVING ref_host IS NOT NULL
ORDER BY hour ASC,day ASC,month ASC,ref_host_cnts DESC;

查询结果

+--------+------+-------+--------------------+----------------+--+
| month  | day  | hour  |      ref_host      | ref_host_cnts  |
+--------+------+-------+--------------------+----------------+--+
| 09     | 18   |  06   | blog.fens.me       | 68             |
| 09     | 18   |  06   | www.angularjs.cn   | 3              |
| 09     | 18   |  06   | www.google.com     | 2              |
| 09     | 18   |  06   | www.baidu.com      | 1              |
| 09     | 18   |  06   | cos.name           | 1              |
| 09     | 18   |  07   | blog.fens.me       | 711            |
| 09     | 18   |  07   | www.google.com.hk  | 20             |
| 09     | 18   |  07   | www.angularjs.cn   | 20             |
| 09     | 18   |  07   | www.dataguru.cn    | 10             |
| 09     | 18   |  07   | www.fens.me        | 6              |
+--------+------+-------+--------------------+----------------+--+

统计pv总量最大的来源TOPN

首先需要了解一下hive 中窗口函数的用法
在hive的窗口函数中可以简单了解

统计一天内每小时各来访的host的url的topN

CREATE TABLE dw_pvs_refhost_topn_everyhour(
hour string,
toporder string,
ref_host string,
ref_host_cnts string)
partitioned by (datestr string);

INSERT INTO TABLE dw_pvs_refhost_topn_everyhour PARTITION (datestr = '20130918')
SELECT t.hour,t.od,t.ref_host,t.ref_host_cnts
FROM (
SELECT ref_host,ref_host_cnts,CONCAT(month,day,hour) AS hour,
ROW_NUMBER() over ( PARTITION by CONCAT(month,day,hour) ORDER BY ref_host_cnts DESC) od
FROM dw_pvs_refererhost_everyhour ) t
WHERE od<=3;

人均浏览页数

第一步：先求有多少人，用一个ip地址来代表一个人，那么就是对ip地址去重

SELECT count(DISTINCT(remote_addr)) FROM ods_weblog_detail;

+-------+--+
|  _c0  |
+-------+--+
| 1027  |
+-------+--+

第二步：求浏览页数总数，再用页数总数除以ip去重数

SELECT '20130918',SUM(b.pvs)/COUNT(b.remote_addr)
FROM (
SELECT remote_addr,count(1) AS pvs 
FROM ods_weblog_detail
WHERE datestr = '20130918'
GROUP BY remote_addr ) b;

受访分析

各页面PV（每个页面受到多少次访问）——使用request字段来代表我们访问的页面

SELECT request AS request,COUNT(request) AS request_counts
FROM ods_weblog_detail
GROUP BY request
HAVING request IS NOT NULL
ORDER BY request_counts DESC
LIMIT 20;

+----------------------------------------------------+-----------------+--+
|                      request                       | request_counts  |
+----------------------------------------------------+-----------------+--+
| /                                                  | 3139            |
| /wp-includes/js/jquery/jquery-migrate.min.js?ver=1.2.1 | 361             |
| /wp-includes/js/jquery/jquery.js?ver=1.10.2        | 358             |
| /js/baidu.js                                       | 318             |
| /wp-admin/admin-ajax.php                           | 308             |
| /js/google.js                                      | 308             |
| /wp-content/themes/silesia/js/jquery.cycle.all.min.js | 293             |
| /wp-content/themes/silesia/functions/js/shortcode.js | 290             |
| /wp-content/themes/silesia/js/load.js              | 290             |
| /wp-includes/js/comment-reply.min.js?ver=3.6       | 285             |
| /feed/                                             | 263             |
| /wp-content/themes/silesia/style.css               | 255             |
| /wp-content/themes/silesia/functions/css/shortcodes.css | 254             |
| /wp-content/themes/silesia/images/slide-bg.png     | 238             |
| /wp-content/themes/silesia/images/natty-logo.png   | 238             |
| /wp-content/themes/silesia/images/crubms-div.png   | 238             |
| /wp-content/themes/silesia/images/ico-twitter.png  | 236             |
| /wp-content/themes/silesia/images/home-ico.png     | 236             |
| /wp-content/themes/silesia/images/ico-meta.gif     | 235             |
| /wp-content/themes/silesia/images/sprites/post-type.png | 233             |
+----------------------------------------------------+-----------------+--+

热门页面统计——统计20130918分区里面受访页面的top10

创建热门表

CREATE TABLE dw_hotpages_everyday(
day stirng,
url string,
pvs string);

热门页面统计

INSERT INTO TABLE dw_hotpages_everyday
SELECT '20130918',a.request,a.request_counts
FROM(
SELECT request AS request,COUNT(request) AS request_counts
FROM ods_weblog_detail
WHERE datestr='20130918'
GROUP BY request
HAVING request IS NOT NULL ) a
ORDER BY a.request_counts DESC
LIMIT 10;

+-----------+----------------------------------------------------+-------+--+
|    day    |                        url                         |  pvs  |
+-----------+----------------------------------------------------+-------+--+
| 20130918  | /                                                  | 3139  |
| 20130918  | /wp-includes/js/jquery/jquery-migrate.min.js?ver=1.2.1 | 361   |
| 20130918  | /wp-includes/js/jquery/jquery.js?ver=1.10.2        | 358   |
| 20130918  | /js/baidu.js                                       | 318   |
| 20130918  | /wp-admin/admin-ajax.php                           | 308   |
| 20130918  | /js/google.js                                      | 308   |
| 20130918  | /wp-content/themes/silesia/js/jquery.cycle.all.min.js | 293   |
| 20130918  | /wp-content/themes/silesia/functions/js/shortcode.js | 290   |
| 20130918  | /wp-content/themes/silesia/js/load.js              | 290   |
| 20130918  | /wp-includes/js/comment-reply.min.js?ver=3.6       | 285   |
+-----------+----------------------------------------------------+-------+--+

统计每天最热门页面的Top10

SELECT CONCAT(a.month,a.day),a.month,a.day,a.request,a.request_counts
FROM (
SELECT month,day,request,COUNT(1) AS request_counts
FROM ods_weblog_detail
WHERE datestr = '20130918'
GROUP BY request,month,day
HAVING request IS NOT NULL
ORDER BY request_counts DESC
LIMIT 10 ) a;

+-------+----------+--------+----------------------------------------------------+-------------------+--+
|  _c0  | a.month  | a.day  |                     a.request                      | a.request_counts  |
+-------+----------+--------+----------------------------------------------------+-------------------+--+
| 0918  | 09       | 18     | /                                                  | 2268              |
| 0919  | 09       | 19     | /                                                  | 871               |
| 0918  | 09       | 18     | /wp-includes/js/jquery/jquery-migrate.min.js?ver=1.2.1 | 293               |
| 0918  | 09       | 18     | /wp-includes/js/jquery/jquery.js?ver=1.10.2        | 290               |
| 0918  | 09       | 18     | /js/baidu.js                                       | 269               |
| 0918  | 09       | 18     | /js/google.js                                      | 259               |
| 0918  | 09       | 18     | /wp-content/themes/silesia/js/jquery.cycle.all.min.js | 244               |
| 0918  | 09       | 18     | /wp-content/themes/silesia/js/load.js              | 243               |
| 0918  | 09       | 18     | /wp-content/themes/silesia/functions/js/shortcode.js | 242               |
| 0918  | 09       | 18     | /wp-includes/js/comment-reply.min.js?ver=3.6       | 223               |
+-------+----------+--------+----------------------------------------------------+-------------------+--+

访客分析

独立访客
按照时间维度比如小时来统计独立访客及其产生的pv
对于独立访客的识别，如果在原始日志中有用户标识，则根据用户标识即很好实现;此处，由于原始日志中并没有用户标识，以访客IP来模拟，技术上是一样的，只是精确度相对较低。

统计每小时的独立访客和产生的pv量

建表

CREATE TABLE dw_user_dstc_ip_h(
remote_addr string,
pvs bigint,
hour string);

统计数据

INSERT INTO dw_user_dstc_ip_h
SELECT remote_addr,count(1) AS pvs,concat(month,day,hour) AS hour
FROM ods_weblog_detail
WHERE datestr = '20130918'
GROUP BY remote_addr,concat(month,day,hour);

每日新访客

创建历日去重访客累积表

CREATE TABLE dw_user_dsct_history(
day string,
ip string)
partitioned by (datestr string);

创建每日新访客表

CREATE TABLE dw_user_new_d(
day string,
ip string)
partitioned by (datestr string);

每日新访客插入到新访客表

INSERT INTO TABLE dw_user_new_d PARTITION (datestr = '20130918')
SELECT tmp.day AS day,tmp.today_addr AS new_ip
FROM (
SELECT today.day AS day,today.remote_addr AS today_addr,old.ip AS old_addr
FROM (
SELECT DISTINCT remote_addr AS remote_addr,"20130918" AS day
FROM ods_weblog_detail
WHERE datestr = '20130918' ) today
LEFT OUTER JOIN dw_user_dsct_history old
ON today.remote_addr = old.ip ) tmp
WHERE tmp.old_addr IS NULL;

每日新访客追加到历日去重访客累积表

INSERT INTO TABLE dw_user_dsct_history
PARTITION (datestr = '20130918')
SELECT day,ip FROM dw_user_new_d
WHERE datestr = '20130918';

访客visit分析

回头/单次访客统计
在这里插入图片描述

创建回头访客表

CREATE TABLE dw_user_returning(
day string,
remote_addr string,
acc_cnt string)
partitioned by (datestr string);

统计回头访客数据

INSERT overwrite TABLE dw_user_returning
PARTITION (datestr = '20130918')
SELECT tmp.day,tmp.remote_addr,tmp.acc_cnt
FROM (
SELECT '20130918' AS day,remote_addr,count(session) AS acc_cnt
FROM ods_click_stream_visit
GROUP BY remote_addr ) tmp
WHERE tmp.acc_cnt > 1;

人均访问频次
就是总visit数除以去重后的总用户数

SELECT SUM(pagevisits)/COUNT(DISTINCT remote_addr)
FROM ods_click_stream_visit
WHERE datestr = '20130918';

关键路径转化率分析（漏斗模型）

见【Hadoop离线基础总结】关键路径转化率分析（漏斗模型）
级联求和

见【Hadoop离线基础总结】Hive级联求和

posted @ 2020-03-13 00:04 _codeRookie 阅读(283) 评论(0) 编辑收藏举报

刷新页面返回顶部

_codeRookie

【Hadoop离线基础总结】流量日志分析网站整体架构模块开发

目录

数据仓库设计

维度建模概述

维度建模的三种模式

本项目中数据仓库的设计

ETL开发

创建ODS层数据表

导入ODS层数据

生成ODS层明细宽表

统计分析开发

流量分析

受访分析

访客visit分析

关键路径转化率分析（漏斗模型）

级联求和

公告